机器学习之SVM实战

最新推荐文章于 2025-05-20 19:18:30 发布

lhaniey

最新推荐文章于 2025-05-20 19:18:30 发布

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分类专栏： python 机器学习数据分析文章标签： SVM分类器实战练习 python 数据挖掘与分析

本文链接：https://blog.youkuaiyun.com/lhy2239705435/article/details/90054959

该博客介绍了如何使用SVM模型预测乳腺癌，通过数据探索、清洗、特征选择、归一化处理和模型训练，最终用linearSVC实现高准确率的分类。

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思路：利用SVM模型，对乳腺癌进行预测

数据集来源：点击此处进行下载

数据表一共包括32字段，代表含义如下：

1、利用以下代码，可以对数据进行初步的探索

from sklearn import svm
import pandas as pd 

#加载数据集
data=pd.read_csv('./data.csv')



pd.set_option('display.max_columns',None)


print(data.info())
print('*'*40)
print(data.describe())
print('*'*40)
print(data.columns)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 569 entries, 0 to 568
Data columns (total 32 columns):
id                         569 non-null int64
diagnosis                  569 non-null object
radius_mean                569 non-null float64
texture_mean               569 non-null float64
perimeter_mean             569 non-null float64
area_mean                  569 non-null float64
smoothness_mean            569 non-null float64
compactness_mean           569 non-null float64
concavity_mean             569 non-null float64
concave points_mean        569 non-null float64
symmetry_mean              569 non-null float64
fractal_dimension_mean     569 non-null float64
radius_se                  569 non-null float64
texture_se                 569 non-null float64
perimeter_se               569 non-null float64
area_se                    569 non-null float64
smoothness_se              569 non-null float64
compactness_se             569 non-null float64
concavity_se               569 non-null float64
concave points_se          569 non-null float64
symmetry_se                569 non-null float64
fractal_dimension_se       569 non-null float64
radius_worst               569 non-null float64
texture_worst              569 non-null float64
perimeter_worst            569 non-null float64
area_worst                 569 non-null float64
smoothness_worst           569 non-null float64
compactness_worst          569 non-null float64
concavity_worst            569 non-null float64
concave points_worst       569 non-null float64
symmetry_worst             569 non-null float64
fractal_dimension_worst    569 non-null float64
dtypes: float64(30), int64(1), object(1)
memory usage: 142.3+ KB
None
****************************************
                 id  radius_mean  texture_mean  perimeter_mean    area_mean  \
count  5.690000e+02   569.000000    569.000000      569.000000   569.000000   
mean   3.037183e+07    14.127292     19.289649       91.969033   654.889104   
std    1.250206e+08     3.524049      4.301036       24.298981   351.914129   
min    8.670000e+03     6.981000      9.710000       43.790000   143.500000   
25%    8.692180e+05    11.700000     16.170000       75.170000   420.300000   
50%    9.060240e+05    13.370000     18.840000       86.240000   551.100000   
75%    8.813129e+06    15.780000     21.800000      104.100000   782.700000   
max    9.113205e+08    28.110000     39.280000      188.500000  2501.000000   

       smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \
count       569.000000        569.000000      569.000000           569.000000   
mean          0.096360          0.104341        0.088799             0.048919   
std           0.014064          0.052813        0.079720             0.038803   
min           0.052630          0.019380        0.000000             0.000000   
25%           0.086370          0.064920        0.029560             0.020310   
50%           0.095870          0.092630        0.061540             0.033500   
75%           0.105300          0.130400        0.130700             0.074000   
max           0.163400          0.345400        0.426800             0.201200   

       symmetry_mean  fractal_dimension_mean   radius_se  texture_se  \
count     569.000000              569.000000  569.000000  569.000000   
mean        0.181162                0.062798    0.405172    1.216853   
std         0.027414                0.007060    0.277313    0.551648   
min         0.106000                0.049960    0.111500    0.360200   
25%         0.161900                0.057700    0.232400    0.833900   
50%         0.179200                0.061540    0.324200    1.108000   
75%         0.195700                0.066120    0.478900    1.474000   
max         0.304000                0.097440    2.873000    4.885000   

       perimeter_se     area_se  smoothness_se  compactness_se  concavity_se  \
count    569.000000  569.000000     569.000000      569.000000    569.000000   
mean       2.866059   40.337079       0.007041        0.025478      0.031894   
std        2.021855   45.491006       0.003003        0.017908      0.030186   
min        0.757000    6.802000       0.001713        0.002252      0.000000   
25%        1.606000   17.85